• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.645-660
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• 2001

In this paper we propose a goodness-of-fit test statistic for testing the (null) parametric model versus the (alternative) nonparametric model. Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. Our test is based on the bootstrap estimator of the probability that the smoothing parameter estimator is infinite when fitting residuals to cubic smoothing spline. Power performance of this test is investigated and is compared with many other tests. Illustrative examples based on real data sets are given.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.551-560
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• 2007

Kruskal and Wallis (1952) proposed typical nonparametric method in one-way layout problem. A special feature of this procedure is use of rank in mixed samples. In this paper, the new procedure based on placement as extension of the two sample placement tests described in Orban and Wolfe (1982) was proposed. Some critical values in small sample cases and comparative results of a Monte Carlo power study are presented.

• Journal of the Korean Statistical Society
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• v.9 no.2
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• pp.181-193
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• 1980

A class of nonparametric estimators of the ratio of scale parameters is proposed. The estimators are based on the distribution-free rank-like test suggested by Fligner and Killeen (1976). An explicit form of the estimator is the median of the ratios of absolute deviations from the combined sample median. A small-sample Monte Carlo study shows that the proposed estimator is more efficient than the Bhattacharyya (1977) estimator. The proposed estimator is is reasonably insensitive to small failures in the assumption of equal medians. A modified estimator is also considered when the meidans are unequal.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.847-856
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• 2013

In this paper, we deal with testing goodness-of-fit for normal distribution based on parametric and nonparametric entropy estimators. The minimum variance unbiased estimator for the entropy of the normal distribution is derived as a parametric entropy estimator to be used for the construction of a test statistic. For a nonparametric entropy estimator of a data-generating distribution under the alternative hypothesis sample entropy and its modifications are used. The critical values of the proposed tests are estimated by Monte Carlo simulations and presented in a tabular form. The performance of the proposed tests under some selected alternatives are investigated by means of simulations. The results report that the proposed tests have better power than the previous entropy-based test by Vasicek (1976). In applications, the new tests are expected to be used as a competitive tool for testing normality.

• Journal of the Korean Statistical Society
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• v.17 no.2
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• pp.61-80
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• 1988

For testing homogeneity of scale parameters aginst ordered alternatives, some nonparametric test statistics based on pairwise ranking method are proposed. The proposed tests are distribution-free. The asymptotic distributions of the proposed statistcs are also investigated. It is shown that the Pitman efficiencies of the proposed rank tests are the same as those of the corresponding two-sample rank tests in the scale problem. A small-sample Monte Carlo study is also performed. The results show that the proposed tests are robust and efficient.

• International Journal of Reliability and Applications
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• v.6 no.1
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• pp.53-64
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• 2005

In this paper, we propose a nonparametric test procedure for the multivariate, grouped and right censored data for two sample problem. For the construction of the test statistic, we use the linear rank statistics for each component and apply the permutation principle for obtaining the null distribution. For the large sample case, the asymptotic distribution is derived under the null hypothesis with the additional assumption that two censoring distributions are also equal. Finally, we illustrate our procedure with an example and discuss some concluding remarks. In appendices, we derive the expression of the covariance matrix and prove the asymptotic distribution.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.721-729
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• 2012

Various methods control the influence of a covariate on a response variable. These methods are analysis of covariance(ANCOVA), RANK ANCOVA, ANOVA of (covariate-adjusted) residuals, and Kruskal-Wallis tests on residuals. Covariate-adjusted residuals are obtained from the overall regression line fit to the entire data set that ignore the treatment levels or factors. It is demonstrated that the methods on covariate-adjusted residuals are only appropriate when the regression lines are parallel and covariate means are equal for all treatments. In this paper, we proposed the new nonparametric method on the ANCOVA model, as applying joint placement in a one-way layout on residuals as described in Chung and Kim (2007). A Monte Carlo simulation study is adapted to compare the power of the proposed procedure with those of the previous procedure.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.237-245
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• 1993

A nonparametric test for the parallelisim of k regression lines against ordered alternatives is proposed. The test statistic is weighted Jonckheere-type statistic applied to slope estimators obtained from each lines. The distribution of the proposed test statistic is asymptotically distribution-free. From the viewpoint of efficiencies, the proposed test desirable properties and is more efficient than the other nonparametric tests.

• Journal of Korean Society for Quality Management
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• v.18 no.1
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• pp.29-39
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• 1990

In this paper we propose a nonparametric test for testing the equality of several regression lines against ordered alternatives, when the independent variables are positive and all regression lines have a common intercept. The proposed test is based on a Jonckheere-type statistic applied to residuals. Under some conditions our proposed test statistic is asymptotically distribution-free. The small-sample powers of our test are compared with other tests by a Monte Carlo study. The simulation results show that the proposed test has significantly higher empirical powers than the other tests considered in this paper.

• Proceedings of the Korean Reliability Society Conference
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• 2004.07a
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• pp.167-175
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• 2004

A nonparametric procedure is proposed to test the exponentiality against the hypothesis that one life distribution has a greater residual life times than the other life distribution. Such a hypothesis turns out to be equivalent to the one that one failure rate is greater than the other and so the proposed test works as a competitor to more IFR tests by Kochar (1979, 1981) and Cheng (1985). Our test statistic utilizes the U-statistics theory and a large sample nonpara metric test is established. The power of the proposed test is discussed by calculating the Pitman asymptotic relative efficiencies against several alter native hypotheses. A numerical example is presented to exemplify the proposed test.